## Matching-range-constrained real-time loop closure detection with CNNs features

• abstract

• loop closure detection 闭环检测LCD
• DCNNs
• Some researchers

• pre-trained CNNs model
• generating an image representation
• appropriate for visual LCD in SLAM
• Differences and Challenges between Simple Computer Vision & Robotic Application

• real-time performance
• in this paper

• making use of the feature generated by CNNs layers

• to implement LCD in real environment
• the first problem

• provide a value to limit the matching range of images
• better results

• improve the real-time performance

• using an efficient feature compression method
• background

• SLAM algorithm aims

• map an unknown environment
• while simultaneously localizing the robot
• LCD

• determine whether a robot is back to previously visited location
• correcting the accumulate error is critical for building a consistent map
• One of the most essential techniques in SLAM
• develop a LCD algorithm

• one class of popular and successful techniques

• based on Matching the current view of the robot with
• Those Corresponding to previously visited locations in the robot map
• OTHER

• image matching problem
• STEPS

• image description
• similarity measurement
• The state-of-the-art Algorithms

• image description

• BoW ： bag-of-words model

• clusters the visual feature descriptors in images

• visual features (Success)

• SIFT
• Surf
• builds the dictionary

• find the corresponding words of image
• similarity measurement

• Challenges still remain

• in dynamical and large-scale environment
• long period of time

• day
• week
• months
• dramatic condition change

• viewpoint change over time

• the hand-craft methods can not deal with these situations very well
• good news

• in recent ML and CV conference
• the features generated by convolutional neural networks outperform well in visual recognition classification and detection applications

• it has been demonstrated to be versatile and transferable
• even though they were trained on a very specific target task
• they can be successfully used to solving different problems
• and may outperform traditional hand-craft features
• Two Challenges appear while we use these features generated by CNNs in pratical environment

• Firstly , the adjacent images in the data-set of LCD might have more resemblance than the images that really form the loop closure

• #?为什么相邻图像比真实构成闭环检测的图像更相似？
• the algorithm tends to identify the adjacent images as loop closure
• Secondly , the feature matching is computationally intensive , because the dimension of features generated by CNNs may be very large .

LCD have to compare the current image to large amount of pre-captured images

        - 太大的计算量不利于实时性

- in this paper

- two solution

- firstly

- provide matching range of candidate images

- #将匹配到一张图片，变成匹配到相似的图片范围里去

- secondly

- a efficient feature compression method

- #有效在于，通过压缩了CNN层得到的图像特征，这样处理的图像变小，处理速度快一点就能提高实时性， （临界性能减小）

- to reduce dimension of feature generated by CNNs


## 遥感影像道路提取研究

### 基本思路

• 首先利用各种特征提取方法提取有用特征
• 应用各种方法找出满足道路特征的道路
• 最后对道路提取结果进行后期优化处理得到最终道路提取结果

### 目前的方法

• 特征提取
• 道路提取

• 1、同时包含道路拓扑结构信息和宽度信息的提取
• 2、只提取出道路中心线的拓扑结构信息

## Long Range Traversable Region Detection Based on Superpixels

Clustering for Mobile Robots

### abstract

• — Traversable region detection
• i传统方法缺点

• only short range traversable regions can be detected
• 原因

• the limited image resolution and baseline of stereo vision
• 本文方法

• detect long range traversable regions without using any supervised or self-supervised learning process
• Superpixels clustering algorithm
• superpixels are clustered using an improved spectral clustering algorithm to segment the image
• integrating short range traversable region detection : u-v-disparity

• then the traversable region can be extended to long range naturally
• result

• works well in different outdoor environments
• detecting range can be improved greatly

### Introduction

• 可达性

• regions that do not contain geometric obstacles
• 采集信息

• ultrasonicsensor
• stereo vision

• measure the ranges to objects
• by calculating disparities between stereo images
• laser scanners

• key

• After acquiring the disparities ,traversable regions or obstacles can be detected robustly and efficiently
(using a series of approaches based on u-v-disparties )
• V-disparity

• Aim

• detect Obstacles
• (u,v)

• 坐标
• ways

• by accumulating pixels with the same disparity value d in each row , a v-disparity image (d,v) was build
• perpendicular obstacles can be mapped to vertical lines

• pixel intensity represents the width of obstacles
• the traversable region modelled as a succession of planes can be projected as slanted line segment